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Do Vision-Language Models See Dwarf Galaxies the Way We Do?
Dimitrios Tanoglidis, Chin Yi Tan, Kate Overdeck +1
With the advent of powerful, general-purpose vision-language models (VLMs), there has been growing interest in their potential to assist astronomical discovery, a field characteriz…
The Vera C. Rubin Observatory Data Preview 1
Vera C Rubin Observatory Team, Tatiana Acero Cuellar, Emily Acosta +325
We present Rubin Data Preview 1 DP1, the first data from the NSF DOE Vera C Rubin Observatory, comprising raw and calibrated single epoch images, coadds, difference images, detecti…
A Path to an All-Sky Survey with Roman
Jiwon Jesse Han, Anirudh Chiti, Kai-Feng Chen +93
A deep, space-based, all-sky near-infrared survey carried out with the Nancy Grace Roman Space Telescope would constitute a foundational astronomical infrastructure for decades to…
X-ray characterization of fully-depleted p-channel Skipper-CCDs for the DarkNESS mission
Phoenix Alpine, Ana M. Botti, Brenda A. Cervantes-Vergara +11
The Dark matter Nanosatellite Equipped with Skipper Sensors (DarkNESS) mission is a 6U CubeSat designed to search for X-ray lines from decaying dark matter using Skipper-CCDs. Thic…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys
Yufeng Luo, Adam D. Myers, Alex Drlica-Wagner +7
As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impr…